{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a0f3f6e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import networkx as nx\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "import geopandas as gpd\n",
    "from coord_convert.transform import wgs2gcj, wgs2bd, gcj2wgs, gcj2bd, bd2wgs, bd2gcj \n",
    "from shapely.geometry import Point,Polygon,shape\n",
    "import shapely.geometry\n",
    "import sys\n",
    "sys.path.append(r'./packages_rely/')\n",
    "import plot_map\n",
    "import json\n",
    "import osmnx as ox\n",
    "from scipy.spatial.distance import pdist\n",
    "from scipy import spatial\n",
    "import math\n",
    "from math import radians, cos, sin, asin, sqrt\n",
    "import osmnx as ox\n",
    "import folium\n",
    "import os\n",
    "import copy\n",
    "from sklearn import preprocessing\n",
    "plt.rc('font',family='Times New Roman')\n",
    "from matplotlib import rcParams\n",
    "\n",
    "config = {\n",
    "    \"font.family\":'serif',\n",
    "    \"font.size\": 10,\n",
    "    \"mathtext.fontset\":'stix',\n",
    "    \"font.serif\": ['FangSong','SimSun'],\n",
    "}\n",
    "rcParams.update(config)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7671008b",
   "metadata": {},
   "source": [
    "# 1.数据读取、预处理及特征提取"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0529abc5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>camera_idxx</th>\n",
       "      <th>camera_addr</th>\n",
       "      <th>in_time_latest</th>\n",
       "      <th>turn_dir</th>\n",
       "      <th>camera_type</th>\n",
       "      <th>camera_service_ip</th>\n",
       "      <th>channel_id</th>\n",
       "      <th>channel_num</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>...</th>\n",
       "      <th>citydogcj02lat</th>\n",
       "      <th>citydogcj02lng</th>\n",
       "      <th>camera_node</th>\n",
       "      <th>node_gcj02lat</th>\n",
       "      <th>node_gcj02lng</th>\n",
       "      <th>lnglat84</th>\n",
       "      <th>lng84</th>\n",
       "      <th>lat84</th>\n",
       "      <th>nodeid</th>\n",
       "      <th>dis2node</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>33010900001320130299xx</td>\n",
       "      <td>风情大道-晨晖路南向北1</td>\n",
       "      <td>2018-11-01 11:28:01.362</td>\n",
       "      <td>南向北</td>\n",
       "      <td>枪机</td>\n",
       "      <td>33.90.129.76</td>\n",
       "      <td>1000053.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>30.142625</td>\n",
       "      <td>120.251083</td>\n",
       "      <td>...</td>\n",
       "      <td>30.136641</td>\n",
       "      <td>120.244824</td>\n",
       "      <td>风情大道-晨晖路</td>\n",
       "      <td>30.137144</td>\n",
       "      <td>120.245001</td>\n",
       "      <td>(120.24035064995653, 30.139100201406947)</td>\n",
       "      <td>120.240351</td>\n",
       "      <td>30.139100</td>\n",
       "      <td>483</td>\n",
       "      <td>51.412587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>33010900001320130298xx</td>\n",
       "      <td>风情大道-晨晖路南向北2</td>\n",
       "      <td>2018-11-01 11:28:31.935</td>\n",
       "      <td>南向北</td>\n",
       "      <td>枪机</td>\n",
       "      <td>33.90.129.76</td>\n",
       "      <td>1000053.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>30.142625</td>\n",
       "      <td>120.251083</td>\n",
       "      <td>...</td>\n",
       "      <td>30.136672</td>\n",
       "      <td>120.244855</td>\n",
       "      <td>风情大道-晨晖路</td>\n",
       "      <td>30.137144</td>\n",
       "      <td>120.245001</td>\n",
       "      <td>(120.2403811675365, 30.139130718984944)</td>\n",
       "      <td>120.240381</td>\n",
       "      <td>30.139131</td>\n",
       "      <td>483</td>\n",
       "      <td>47.168040</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              camera_idxx   camera_addr          in_time_latest turn_dir  \\\n",
       "0  33010900001320130299xx  风情大道-晨晖路南向北1 2018-11-01 11:28:01.362      南向北   \n",
       "1  33010900001320130298xx  风情大道-晨晖路南向北2 2018-11-01 11:28:31.935      南向北   \n",
       "\n",
       "  camera_type camera_service_ip  channel_id  channel_num        lat  \\\n",
       "0          枪机      33.90.129.76   1000053.0         15.0  30.142625   \n",
       "1          枪机      33.90.129.76   1000053.0         14.0  30.142625   \n",
       "\n",
       "          lng  ... citydogcj02lat  citydogcj02lng camera_node node_gcj02lat  \\\n",
       "0  120.251083  ...      30.136641      120.244824    风情大道-晨晖路     30.137144   \n",
       "1  120.251083  ...      30.136672      120.244855    风情大道-晨晖路     30.137144   \n",
       "\n",
       "   node_gcj02lng                                  lnglat84       lng84  \\\n",
       "0     120.245001  (120.24035064995653, 30.139100201406947)  120.240351   \n",
       "1     120.245001   (120.2403811675365, 30.139130718984944)  120.240381   \n",
       "\n",
       "       lat84  nodeid   dis2node  \n",
       "0  30.139100     483  51.412587  \n",
       "1  30.139131     483  47.168040  \n",
       "\n",
       "[2 rows x 24 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 电警与交叉口映射\n",
    "# 设备信息表1\n",
    "dev_df = pd.read_excel(\"./数据/distinct_camera.xlsx\")\n",
    "dev_df.dropna(how='all',inplace=True,axis=0)\n",
    "dev_df.dropna(how='all',inplace=True,axis=1)\n",
    "# 将电警卡口的经纬度转化为84坐标系\n",
    "def conv284(ser):\n",
    "    return gcj2wgs(ser[0],ser[1])\n",
    "    \n",
    "dev_df['lnglat84'] = dev_df[['citydogcj02lng','citydogcj02lat']].apply(conv284 , axis=1)\n",
    "dev_df['lng84'] = dev_df[['lnglat84']].applymap(lambda x:x[0])\n",
    "dev_df['lat84'] = dev_df[['lnglat84']].applymap(lambda x:x[1])\n",
    "# 读取网络\n",
    "G = ox.io.load_graphml(filepath='./数据/基于osmnx的路网爬取/graph84坐标系.graphml')\n",
    "# 将交叉口与电警做映射，算法是利用直线距离最近来匹配\n",
    "nn, dist = ox.distance.nearest_nodes(G, dev_df['lng84'], dev_df['lat84'], return_dist=True)\n",
    "dev_df['nodeid'] = nn ; dev_df['dis2node'] = dist\n",
    "# 将电警与交叉口的映射可视化\n",
    "dev_df = dev_df[dev_df['dis2node'] < 100]\n",
    "dev_df.reset_index(drop=True,inplace=True)\n",
    "dev_df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2b6c424a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "正在处理0318日期的数据\n",
      "正在处理0319日期的数据\n",
      "正在处理0320日期的数据\n",
      "正在处理0321日期的数据\n",
      "正在处理0322日期的数据\n",
      "平均每天总行数为7001472.6行，平均每天错误检测占比为0.09101036606529886，平均每天重复检测占比为0.025011387345187968\n"
     ]
    }
   ],
   "source": [
    "# 车牌识别数据概述\n",
    "tol_len = []\n",
    "error_ratio = []\n",
    "detec_times = []\n",
    "for date in ['0318','0319','0320','0321','0322']:\n",
    "    print(\"正在处理{0}日期的数据\".format(date))\n",
    "    # 读取数据并做预处理，按照天为单位读取并处理\n",
    "    path = './数据/萧山市车牌识别数据/'\n",
    "    df = pd.read_csv(path+date+\".csv\")\n",
    "    df.drop(columns=['Unnamed: 0'],inplace=True)\n",
    "    df.rename(columns={'0':'car_num','1':'cap_date','2':'type','3':'dev_id','4':'dir','5':'road_id','6':'turn_id'},inplace=True)\n",
    "    tol_len.append(len(df))\n",
    "    error_ratio.append( len(df[df['car_num'] == 'UNKNOW']) / len(df) )\n",
    "    df = df[df['car_num'] != 'UNKNOW']\n",
    "    len_1 = len(df)\n",
    "    df = df.drop_duplicates(subset=['car_num' , 'cap_date'])\n",
    "    detec_times.append( (len_1-len(df))/len(df) )\n",
    "print(\"平均每天总行数为{0}行，平均每天错误检测占比为{1}，平均每天重复检测占比为{2}\".format(np.mean(tol_len) , \n",
    "                                                          np.mean(error_ratio) , np.mean(detec_times)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b218cb81",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "正在处理0318日期的数据\n",
      "重复数据占比为0.023714642807116625\n",
      "正在处理0319日期的数据\n",
      "重复数据占比为0.024948170492255086\n",
      "正在处理0320日期的数据\n",
      "重复数据占比为0.02493012859810974\n",
      "正在处理0321日期的数据\n",
      "重复数据占比为0.022113746913142385\n",
      "正在处理0322日期的数据\n",
      "重复数据占比为0.026288637368022148\n"
     ]
    },
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>car_num</th>\n",
       "      <th>peak0318</th>\n",
       "      <th>nonpeak0318</th>\n",
       "      <th>peak0319</th>\n",
       "      <th>nonpeak0319</th>\n",
       "      <th>peak0320</th>\n",
       "      <th>nonpeak0320</th>\n",
       "      <th>peak0321</th>\n",
       "      <th>nonpeak0321</th>\n",
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      "text/plain": [
       "   car_num  peak0318  nonpeak0318  peak0319  nonpeak0319  peak0320  \\\n",
       "0  云A00L27       1.0          0.0       0.0          0.0       0.0   \n",
       "1  云A088QL       2.0          0.0       0.0          0.0       0.0   \n",
       "\n",
       "   nonpeak0320  peak0321  nonpeak0321  peak0322  nonpeak0322  \n",
       "0          0.0       0.0          0.0       0.0          0.0  \n",
       "1          0.0       0.0          0.0       0.0          0.0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all = pd.DataFrame([])\n",
    "for date in ['0318','0319','0320','0321','0322']:\n",
    "    print(\"正在处理{0}日期的数据\".format(date))\n",
    "    # 读取数据并做预处理，按照天为单位读取并处理\n",
    "    path = './数据/萧山市车牌识别数据/'\n",
    "    df = pd.read_csv(path+date+\".csv\")\n",
    "    df.drop(columns=['Unnamed: 0'],inplace=True)\n",
    "    df.rename(columns={'0':'car_num','1':'cap_date','2':'type','3':'dev_id','4':'dir','5':'road_id','6':'turn_id'},inplace=True)\n",
    "    # 删除识别错误的车牌\n",
    "    df = df[df['car_num'] != 'UNKNOW']\n",
    "    # 解决车辆在同一时间同一地点被重复检测问题\n",
    "    len1 = len(df)\n",
    "    df = df.drop_duplicates(subset=['car_num' , 'cap_date'])\n",
    "    print(\"重复数据占比为{0}\".format((len1-len(df)) / len1))\n",
    "    # 统计各个交叉口的分时段流量\n",
    "    #将电警表和过车数据表merge起来\n",
    "    df = pd.merge(df[['car_num','cap_date','dev_id']],dev_df[['camera_id','nodeid','lng84','lat84']],how = 'inner',left_on='dev_id',right_on='camera_id')\n",
    "    df.drop(columns=['camera_id'] , inplace=True)\n",
    "    # 早晚高峰出行天数比例、早晚高峰出行频次特征提取\n",
    "    # 提取出早晚高峰数据\n",
    "    df['ana_t'] = df[['cap_date']].applymap(lambda x:x[11:16])\n",
    "    df_peak = df[((df['ana_t'] >= '06:30')&(df['ana_t'] <= '10:00')) | ((df['ana_t'] >= '17:00')&(df['ana_t'] <= '19:30'))]\n",
    "    df_peak = df_peak.groupby(['car_num'])[['cap_date']].count().reset_index().rename(columns={'cap_date':'peak'+date})\n",
    "    # 提取出平峰时段数据\n",
    "    df_nonpeak = df[(df['ana_t']<'06:30')|(df['ana_t']>'19:30')|((df['ana_t'] >'10:00')&(df['ana_t']<='17:00'))]\n",
    "    df_nonpeak = df_nonpeak.groupby(['car_num'])[['cap_date']].count().reset_index().rename(columns={'cap_date':'nonpeak'+date})\n",
    "    df = pd.merge(df_peak,df_nonpeak,how='outer',on='car_num')\n",
    "    df = df.fillna(0)\n",
    "    if len(df_all) == 0:\n",
    "        df_all = copy.deepcopy(df)\n",
    "    else:\n",
    "        df_all = pd.merge(df_all , df , how='outer' , on='car_num')\n",
    "df_all = df_all.fillna(0)\n",
    "df_all.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "af3d083c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "已经处理至0.0%。\n",
      "已经处理至4.11%。\n",
      "已经处理至8.23%。\n",
      "已经处理至12.34%。\n",
      "已经处理至16.46%。\n",
      "已经处理至20.57%。\n",
      "已经处理至24.69%。\n",
      "已经处理至28.8%。\n",
      "已经处理至32.92%。\n",
      "已经处理至37.03%。\n",
      "已经处理至41.15%。\n",
      "已经处理至45.26%。\n",
      "已经处理至49.38%。\n",
      "已经处理至53.49%。\n",
      "已经处理至57.61%。\n",
      "已经处理至61.72%。\n",
      "已经处理至65.84%。\n",
      "已经处理至69.95%。\n",
      "已经处理至74.06%。\n",
      "已经处理至78.18%。\n",
      "已经处理至82.29%。\n",
      "已经处理至86.41%。\n",
      "已经处理至90.52%。\n",
      "已经处理至94.64%。\n",
      "已经处理至98.75%。\n"
     ]
    },
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>(2.0, nan, 1, 0, 1, 2.0)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   car_num  peak0318  nonpeak0318  peak0319  nonpeak0319  peak0320  \\\n",
       "0  云A00L27       1.0          0.0       0.0          0.0       0.0   \n",
       "1  云A088QL       2.0          0.0       0.0          0.0       0.0   \n",
       "\n",
       "   nonpeak0320  peak0321  nonpeak0321  peak0322  nonpeak0322  \\\n",
       "0          0.0       0.0          0.0       0.0          0.0   \n",
       "1          0.0       0.0          0.0       0.0          0.0   \n",
       "\n",
       "                      feats  \n",
       "0  (1.0, nan, 1, 0, 1, 1.0)  \n",
       "1  (2.0, nan, 1, 0, 1, 2.0)  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len1 = len(df_all)\n",
    "# 特征提取\n",
    "def getfeat(ser):\n",
    "    if ser.name % 100000 == 0:\n",
    "        print(\"已经处理至{0}%。\".format(round(ser.name / len1 *100, 2)))\n",
    "    # 将高峰和非高峰的被检测频次取均值\n",
    "    fre_peak = ser[['peak0318','peak0319','peak0320','peak0321','peak0322']][ser[['peak0318','peak0319','peak0320','peak0321','peak0322']]!=0].mean()\n",
    "    fre_nonpeak = ser[['nonpeak0318','nonpeak0319','nonpeak0320','nonpeak0321','nonpeak0322']][ser[['nonpeak0318','nonpeak0319','nonpeak0320','nonpeak0321','nonpeak0322']]!=0].mean()\n",
    "    # 提取高峰和非高峰出行天数\n",
    "    traday_nonpeak = len(ser[['nonpeak0318','nonpeak0319','nonpeak0320','nonpeak0321','nonpeak0322']][ser[['nonpeak0318','nonpeak0319','nonpeak0320','nonpeak0321','nonpeak0322']]!=0])\n",
    "    traday_peak = len(ser[['peak0318','peak0319','peak0320','peak0321','peak0322']][ser[['peak0318','peak0319','peak0320','peak0321','peak0322']]!=0])\n",
    "    # 出行天数和平均每天的被检测频次\n",
    "    tmp_list = []\n",
    "    traday =  0\n",
    "    for t in ['0318','0319','0320','0321','0322']:\n",
    "        tmp = sum(ser[['peak'+t,'nonpeak'+t]])\n",
    "        if tmp != 0:\n",
    "            traday+=1\n",
    "        tmp_list.append(tmp)\n",
    "    fre_avg = sum(tmp_list) / traday\n",
    "    return (fre_peak,fre_nonpeak,traday_peak,traday_nonpeak,traday,fre_avg)\n",
    "df_all['feats'] = df_all.apply(getfeat , axis = 1)\n",
    "df_all.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e8976949",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>car_num</th>\n",
       "      <th>peak0318</th>\n",
       "      <th>nonpeak0318</th>\n",
       "      <th>peak0319</th>\n",
       "      <th>nonpeak0319</th>\n",
       "      <th>peak0320</th>\n",
       "      <th>nonpeak0320</th>\n",
       "      <th>peak0321</th>\n",
       "      <th>nonpeak0321</th>\n",
       "      <th>peak0322</th>\n",
       "      <th>nonpeak0322</th>\n",
       "      <th>feats</th>\n",
       "      <th>fre_peak</th>\n",
       "      <th>fre_nonpeak</th>\n",
       "      <th>traday_peak</th>\n",
       "      <th>traday_nonpeak</th>\n",
       "      <th>traday</th>\n",
       "      <th>fre_avg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>云A00L27</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>云A088QL</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>(2.0, nan, 1, 0, 1, 2.0)</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   car_num  peak0318  nonpeak0318  peak0319  nonpeak0319  peak0320  \\\n",
       "0  云A00L27       1.0          0.0       0.0          0.0       0.0   \n",
       "1  云A088QL       2.0          0.0       0.0          0.0       0.0   \n",
       "\n",
       "   nonpeak0320  peak0321  nonpeak0321  peak0322  nonpeak0322  \\\n",
       "0          0.0       0.0          0.0       0.0          0.0   \n",
       "1          0.0       0.0          0.0       0.0          0.0   \n",
       "\n",
       "                      feats  fre_peak  fre_nonpeak  traday_peak  \\\n",
       "0  (1.0, nan, 1, 0, 1, 1.0)       1.0          NaN            1   \n",
       "1  (2.0, nan, 1, 0, 1, 2.0)       2.0          NaN            1   \n",
       "\n",
       "   traday_nonpeak  traday  fre_avg  \n",
       "0               0       1      1.0  \n",
       "1               0       1      2.0  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all['fre_peak'] = df_all[['feats']].applymap(lambda x:x[0])\n",
    "df_all['fre_nonpeak'] = df_all[['feats']].applymap(lambda x:x[1])\n",
    "df_all['traday_peak'] = df_all[['feats']].applymap(lambda x:x[2])\n",
    "df_all['traday_nonpeak'] = df_all[['feats']].applymap(lambda x:x[3])\n",
    "df_all['traday'] = df_all[['feats']].applymap(lambda x:x[4])\n",
    "df_all['fre_avg'] = df_all[['feats']].applymap(lambda x:x[5])\n",
    "# 将提取的feature保存起来\n",
    "df_all.to_csv(\"./数据/车辆画像特征.csv\",index=False,encoding='gbk')\n",
    "df_all.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4065c12",
   "metadata": {},
   "source": [
    "# 2.特征分布分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a677bce7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>car_num</th>\n",
       "      <th>peak0318</th>\n",
       "      <th>nonpeak0318</th>\n",
       "      <th>peak0319</th>\n",
       "      <th>nonpeak0319</th>\n",
       "      <th>peak0320</th>\n",
       "      <th>nonpeak0320</th>\n",
       "      <th>peak0321</th>\n",
       "      <th>nonpeak0321</th>\n",
       "      <th>peak0322</th>\n",
       "      <th>nonpeak0322</th>\n",
       "      <th>feats</th>\n",
       "      <th>fre_peak</th>\n",
       "      <th>fre_nonpeak</th>\n",
       "      <th>traday_peak</th>\n",
       "      <th>traday_nonpeak</th>\n",
       "      <th>traday</th>\n",
       "      <th>fre_avg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>云A00L27</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>云A088QL</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>(2.0, nan, 1, 0, 1, 2.0)</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   car_num  peak0318  nonpeak0318  peak0319  nonpeak0319  peak0320  \\\n",
       "0  云A00L27       1.0          0.0       0.0          0.0       0.0   \n",
       "1  云A088QL       2.0          0.0       0.0          0.0       0.0   \n",
       "\n",
       "   nonpeak0320  peak0321  nonpeak0321  peak0322  nonpeak0322  \\\n",
       "0          0.0       0.0          0.0       0.0          0.0   \n",
       "1          0.0       0.0          0.0       0.0          0.0   \n",
       "\n",
       "                      feats  fre_peak  fre_nonpeak  traday_peak  \\\n",
       "0  (1.0, nan, 1, 0, 1, 1.0)       1.0          0.0            1   \n",
       "1  (2.0, nan, 1, 0, 1, 2.0)       2.0          0.0            1   \n",
       "\n",
       "   traday_nonpeak  traday  fre_avg  \n",
       "0               0       1      1.0  \n",
       "1               0       1      2.0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv(\"./数据/车辆画像特征.csv\",encoding='gbk')\n",
    "df.fillna(0,inplace=True)\n",
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5a2d2d65",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, '$N_{t}$')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2100x600 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.dpi'] = 150 #分辨率\n",
    "fig = plt.figure(figsize=(14,4))\n",
    "ax1 = plt.subplot(131)\n",
    "ax1.set_xlabel(\"$N_{tp}$\")\n",
    "ax1.set_ylabel(\"车辆数\")\n",
    "ax1.hist(df['traday_peak'],bins = 5)\n",
    "ax2 = plt.subplot(132)\n",
    "ax2.hist(df['traday_nonpeak'],bins=5)\n",
    "ax2.set_xlabel(\"$N_{to}$\")\n",
    "ax3 = plt.subplot(133)\n",
    "ax3.hist(df['traday'],bins=5)\n",
    "ax3.set_xlabel(\"$N_{t}$\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fb0ec2c6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1558287 872028\n"
     ]
    }
   ],
   "source": [
    "# 将出行只有一天的车辆去除\n",
    "df_tem=df[df['traday'] == 1]\n",
    "df_other=df[df['traday'] > 1]\n",
    "print(len(df_tem),len(df_other))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4280f51b",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, '$F_{t}$')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2100x1200 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.dpi'] = 150 #分辨率\n",
    "fig = plt.figure(figsize=(14,8))\n",
    "ax1 = plt.subplot(231)\n",
    "ax1.set_xlabel(\"Travel days in the peak hours\")\n",
    "ax1.set_ylabel(\"车辆数\")\n",
    "ax1.hist(df_other['traday_peak'],bins = 4)\n",
    "ax2 = plt.subplot(232)\n",
    "ax2.hist(df_other['traday_nonpeak'],bins=4)\n",
    "ax2.set_xlabel(\"Travel days in the off-peak hours\")\n",
    "ax3 = plt.subplot(233)\n",
    "ax3.hist(df_other['traday'],bins=4)\n",
    "ax3.set_xlabel(\"Travel days\")\n",
    "ax4 = plt.subplot(234)\n",
    "ax4.hist(df_other[df_other['fre_peak'] <= 50]['fre_peak'],bins=50)\n",
    "ax4.set_xlabel(\"$F_{tp}$\")\n",
    "ax4.set_ylabel(\"车辆数\")\n",
    "ax5 = plt.subplot(235)\n",
    "ax5.hist(df_other[df_other['fre_nonpeak'] <= 50]['fre_nonpeak'],bins=50)\n",
    "ax5.set_xlabel(\"$F_{to}$\")\n",
    "ax6 = plt.subplot(236)\n",
    "ax6.hist(df_other[df_other['fre_avg'] <= 50]['fre_avg'],bins=50)\n",
    "ax6.set_xlabel(\"$F_{t}$\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e35724e0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "430840 441188\n"
     ]
    }
   ],
   "source": [
    "# 将仅仅给检测到1-2次的车辆去除，这部分车辆往往是过境车辆\n",
    "df_transit = df_other[df_other['fre_avg']<=2]\n",
    "df_ana = df_other[df_other['fre_avg']>2]\n",
    "print(len(df_transit),len(df_ana))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "16818bf5",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Detected times')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2100x1200 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.dpi'] = 150 #分辨率\n",
    "fig = plt.figure(figsize=(14,8))\n",
    "ax1 = plt.subplot(231)\n",
    "ax1.set_xlabel(\"Travel days in the peak hours\")\n",
    "ax1.set_ylabel(\"Vehicles\")\n",
    "ax1.hist(df_ana['traday_peak'],bins = 4)\n",
    "ax2 = plt.subplot(232)\n",
    "ax2.hist(df_ana['traday_nonpeak'],bins=4)\n",
    "ax2.set_xlabel(\"Travel days in the off-peak hours\")\n",
    "ax3 = plt.subplot(233)\n",
    "ax3.hist(df_ana['traday'],bins=4)\n",
    "ax3.set_xlabel(\"Travel days\")\n",
    "ax4 = plt.subplot(234)\n",
    "ax4.hist(df_ana[df_ana['fre_peak'] <= 50]['fre_peak'],bins=50)\n",
    "ax4.set_xlabel(\"Detected times in the peak hours\")\n",
    "ax4.set_ylabel(\"Vehicles\")\n",
    "ax5 = plt.subplot(235)\n",
    "ax5.hist(df_ana[df_ana['fre_nonpeak'] <= 50]['fre_nonpeak'],bins=50)\n",
    "ax5.set_xlabel(\"Detected times in the off-peak hours\")\n",
    "ax6 = plt.subplot(236)\n",
    "ax6.hist(df_ana[df_ana['fre_avg'] <= 50]['fre_avg'],bins=50)\n",
    "ax6.set_xlabel(\"Detected times\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93048d30",
   "metadata": {},
   "source": [
    "# 3.聚类分析\n",
    "## 3.1确定最优聚类数目\n",
    "由于总样本量较大，共包含279973辆车，因此确定聚类数目的时候不使用整个样本，以10%抽样率进行随机抽样，然后利用敏感性分析确定最优初始聚类数目。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3387e011",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.62662672, -0.51761603, -1.45297613, -0.11809905, -0.58919283,\n",
       "        -0.73881218],\n",
       "       [-0.78880105, -0.66103545,  1.19961542,  1.91854409,  1.06540749,\n",
       "        -0.71279947],\n",
       "       [-0.25594253,  0.19948104,  1.19961542, -0.7969801 ,  1.06540749,\n",
       "        -0.33821642],\n",
       "       ...,\n",
       "       [ 0.55492913, -0.84356925,  1.19961542, -1.47586115,  1.06540749,\n",
       "         0.0831895 ],\n",
       "       [-0.82934463, -0.615402  ,  0.53646754,  1.23966304,  1.06540749,\n",
       "        -0.75962235],\n",
       "       [-0.25015059, -0.45242539,  0.53646754, -0.7969801 ,  1.06540749,\n",
       "        -0.50990032]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 归一化处理\n",
    "# 对拓扑重要性和实际重要性都进行归一化处理\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.metrics import silhouette_score\n",
    "df_sample = df_ana.sample(frac=0.1,axis=0,random_state=0)\n",
    "# 对数据进行min max标准化\n",
    "scaler = preprocessing.StandardScaler()\n",
    "data = scaler.fit_transform(df_sample[['fre_peak','fre_nonpeak','traday_peak','traday_nonpeak','traday','fre_avg']].values)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "41fbd6f1",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Anaconda\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:788: FutureWarning: 'precompute_distances' was deprecated in version 0.23 and will be removed in 1.0 (renaming of 0.25). It has no effect\n",
      "  \". It has no effect\", FutureWarning)\n",
      "E:\\Anaconda\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:793: FutureWarning: 'n_jobs' was deprecated in version 0.23 and will be removed in 1.0 (renaming of 0.25).\n",
      "  \" removed in 1.0 (renaming of 0.25).\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2 clustering method has been successfully run\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Anaconda\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:788: FutureWarning: 'precompute_distances' was deprecated in version 0.23 and will be removed in 1.0 (renaming of 0.25). It has no effect\n",
      "  \". It has no effect\", FutureWarning)\n",
      "E:\\Anaconda\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:793: FutureWarning: 'n_jobs' was deprecated in version 0.23 and will be removed in 1.0 (renaming of 0.25).\n",
      "  \" removed in 1.0 (renaming of 0.25).\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3 clustering method has been successfully run\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Anaconda\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:788: FutureWarning: 'precompute_distances' was deprecated in version 0.23 and will be removed in 1.0 (renaming of 0.25). It has no effect\n",
      "  \". It has no effect\", FutureWarning)\n",
      "E:\\Anaconda\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:793: FutureWarning: 'n_jobs' was deprecated in version 0.23 and will be removed in 1.0 (renaming of 0.25).\n",
      "  \" removed in 1.0 (renaming of 0.25).\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4 clustering method has been successfully run\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Anaconda\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:788: FutureWarning: 'precompute_distances' was deprecated in version 0.23 and will be removed in 1.0 (renaming of 0.25). It has no effect\n",
      "  \". It has no effect\", FutureWarning)\n",
      "E:\\Anaconda\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:793: FutureWarning: 'n_jobs' was deprecated in version 0.23 and will be removed in 1.0 (renaming of 0.25).\n",
      "  \" removed in 1.0 (renaming of 0.25).\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 clustering method has been successfully run\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Anaconda\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:788: FutureWarning: 'precompute_distances' was deprecated in version 0.23 and will be removed in 1.0 (renaming of 0.25). It has no effect\n",
      "  \". It has no effect\", FutureWarning)\n",
      "E:\\Anaconda\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:793: FutureWarning: 'n_jobs' was deprecated in version 0.23 and will be removed in 1.0 (renaming of 0.25).\n",
      "  \" removed in 1.0 (renaming of 0.25).\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6 clustering method has been successfully run\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Anaconda\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:788: FutureWarning: 'precompute_distances' was deprecated in version 0.23 and will be removed in 1.0 (renaming of 0.25). It has no effect\n",
      "  \". It has no effect\", FutureWarning)\n",
      "E:\\Anaconda\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:793: FutureWarning: 'n_jobs' was deprecated in version 0.23 and will be removed in 1.0 (renaming of 0.25).\n",
      "  \" removed in 1.0 (renaming of 0.25).\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7 clustering method has been successfully run\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Anaconda\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:788: FutureWarning: 'precompute_distances' was deprecated in version 0.23 and will be removed in 1.0 (renaming of 0.25). It has no effect\n",
      "  \". It has no effect\", FutureWarning)\n",
      "E:\\Anaconda\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:793: FutureWarning: 'n_jobs' was deprecated in version 0.23 and will be removed in 1.0 (renaming of 0.25).\n",
      "  \" removed in 1.0 (renaming of 0.25).\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8 clustering method has been successfully run\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Anaconda\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:788: FutureWarning: 'precompute_distances' was deprecated in version 0.23 and will be removed in 1.0 (renaming of 0.25). It has no effect\n",
      "  \". It has no effect\", FutureWarning)\n",
      "E:\\Anaconda\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:793: FutureWarning: 'n_jobs' was deprecated in version 0.23 and will be removed in 1.0 (renaming of 0.25).\n",
      "  \" removed in 1.0 (renaming of 0.25).\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9 clustering method has been successfully run\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Anaconda\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:788: FutureWarning: 'precompute_distances' was deprecated in version 0.23 and will be removed in 1.0 (renaming of 0.25). It has no effect\n",
      "  \". It has no effect\", FutureWarning)\n",
      "E:\\Anaconda\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:793: FutureWarning: 'n_jobs' was deprecated in version 0.23 and will be removed in 1.0 (renaming of 0.25).\n",
      "  \" removed in 1.0 (renaming of 0.25).\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 clustering method has been successfully run\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Anaconda\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:788: FutureWarning: 'precompute_distances' was deprecated in version 0.23 and will be removed in 1.0 (renaming of 0.25). It has no effect\n",
      "  \". It has no effect\", FutureWarning)\n",
      "E:\\Anaconda\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:793: FutureWarning: 'n_jobs' was deprecated in version 0.23 and will be removed in 1.0 (renaming of 0.25).\n",
      "  \" removed in 1.0 (renaming of 0.25).\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11 clustering method has been successfully run\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Anaconda\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:788: FutureWarning: 'precompute_distances' was deprecated in version 0.23 and will be removed in 1.0 (renaming of 0.25). It has no effect\n",
      "  \". It has no effect\", FutureWarning)\n",
      "E:\\Anaconda\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:793: FutureWarning: 'n_jobs' was deprecated in version 0.23 and will be removed in 1.0 (renaming of 0.25).\n",
      "  \" removed in 1.0 (renaming of 0.25).\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12 clustering method has been successfully run\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.dpi'] = 150 #分辨率\n",
    "Scores = [] # 存储不同初始聚类数目的轮廓系数值\n",
    "for k in range(2,13):  \n",
    "    estimator = KMeans(n_clusters=k,init='k-means++', n_init=10, max_iter=300, tol=0.0001, \n",
    "                       precompute_distances='auto', verbose=0, random_state=0, copy_x=True, n_jobs=-1, \n",
    "                       algorithm='auto')  # 构造聚类器  \n",
    "    labels = estimator.fit_predict(data)  \n",
    "    Scores.append(silhouette_score(data , labels , random_state=0))  \n",
    "    print(\"{0} clustering method has been successfully run\".format(k))\n",
    "X = range(2,13)  \n",
    "plt.xlabel('聚类数目')  \n",
    "plt.ylabel('轮廓系数')  \n",
    "plt.plot(X,Scores,'o-')  \n",
    "plt.show()  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f83721a4",
   "metadata": {},
   "source": [
    "## 3.2聚类分析，进而分析各个簇的出行行为"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "2a5c0713",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Anaconda\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:788: FutureWarning: 'precompute_distances' was deprecated in version 0.23 and will be removed in 1.0 (renaming of 0.25). It has no effect\n",
      "  \". It has no effect\", FutureWarning)\n",
      "E:\\Anaconda\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:793: FutureWarning: 'n_jobs' was deprecated in version 0.23 and will be removed in 1.0 (renaming of 0.25).\n",
      "  \" removed in 1.0 (renaming of 0.25).\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.3355986372522594\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:10: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  # Remove the CWD from sys.path while we load stuff.\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>car_num</th>\n",
       "      <th>peak0318</th>\n",
       "      <th>nonpeak0318</th>\n",
       "      <th>peak0319</th>\n",
       "      <th>nonpeak0319</th>\n",
       "      <th>peak0320</th>\n",
       "      <th>nonpeak0320</th>\n",
       "      <th>peak0321</th>\n",
       "      <th>nonpeak0321</th>\n",
       "      <th>peak0322</th>\n",
       "      <th>nonpeak0322</th>\n",
       "      <th>feats</th>\n",
       "      <th>fre_peak</th>\n",
       "      <th>fre_nonpeak</th>\n",
       "      <th>traday_peak</th>\n",
       "      <th>traday_nonpeak</th>\n",
       "      <th>traday</th>\n",
       "      <th>fre_avg</th>\n",
       "      <th>labels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>云A114QQ</td>\n",
       "      <td>2.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>(6.4, 16.4, 5, 5, 5, 22.8)</td>\n",
       "      <td>6.4</td>\n",
       "      <td>16.40</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>22.8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>云A1J1J9</td>\n",
       "      <td>6.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>(4.5, 5.75, 2, 4, 5, 6.4)</td>\n",
       "      <td>4.5</td>\n",
       "      <td>5.75</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6.4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   car_num  peak0318  nonpeak0318  peak0319  nonpeak0319  peak0320  \\\n",
       "3  云A114QQ       2.0         27.0       3.0          7.0       9.0   \n",
       "6  云A1J1J9       6.0         17.0       3.0          0.0       0.0   \n",
       "\n",
       "   nonpeak0320  peak0321  nonpeak0321  peak0322  nonpeak0322  \\\n",
       "3          9.0      13.0         33.0       5.0          6.0   \n",
       "6          4.0       0.0          1.0       0.0          1.0   \n",
       "\n",
       "                        feats  fre_peak  fre_nonpeak  traday_peak  \\\n",
       "3  (6.4, 16.4, 5, 5, 5, 22.8)       6.4        16.40            5   \n",
       "6   (4.5, 5.75, 2, 4, 5, 6.4)       4.5         5.75            2   \n",
       "\n",
       "   traday_nonpeak  traday  fre_avg  labels  \n",
       "3               5       5     22.8       1  \n",
       "6               4       5      6.4       1  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 将整个数据集标准化\n",
    "scaler = preprocessing.StandardScaler()\n",
    "data = scaler.fit_transform(df_ana[['fre_peak','fre_nonpeak','traday_peak','traday_nonpeak','traday','fre_avg']].values)\n",
    "# 聚类成3个簇看一下结果\n",
    "estimator = KMeans(n_clusters=3,init='k-means++', n_init=10, max_iter=300, tol=0.0001, \n",
    "                       precompute_distances='auto', verbose=0, random_state=0, copy_x=True, n_jobs=-1, \n",
    "                       algorithm='auto')  # 构造聚类器\n",
    "labels = estimator.fit_predict(data)\n",
    "print(silhouette_score(data , labels , random_state=0))\n",
    "df_ana['labels'] = labels\n",
    "df_ana.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "44b6be3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 将各种结果都保存下\n",
    "path = './数据/车辆画像结果/'\n",
    "df_tem.to_csv(path+\"临时办事车辆.csv\",index=False,encoding='gbk')  # 一周内仅仅出现一天的车辆\n",
    "df_transit.to_csv(path+'频繁过境车辆.csv',index=False,encoding='gbk') # 一周出现大于一天，日均被检测频次小于等于2次的车辆\n",
    "df_ana.to_csv(path+\"聚类画像结果.csv\",index=False,encoding='gbk') # 聚类画像结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "6622a000",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            fre_peak    fre_nonpeak    traday_peak  traday_nonpeak  \\\n",
      "count  193826.000000  193826.000000  193826.000000   193826.000000   \n",
      "mean        5.790429       5.549788       1.796565        1.530785   \n",
      "std         5.270249       5.697408       0.848259        0.971135   \n",
      "min         0.000000       0.000000       0.000000        0.000000   \n",
      "25%         2.000000       1.333333       1.000000        1.000000   \n",
      "50%         4.000000       4.000000       2.000000        2.000000   \n",
      "75%         8.000000       8.000000       2.000000        2.000000   \n",
      "max        46.000000      51.000000       3.000000        5.000000   \n",
      "\n",
      "              traday        fre_avg  \n",
      "count  193826.000000  193826.000000  \n",
      "mean        2.512336       8.567697  \n",
      "std         0.616966       6.053068  \n",
      "min         2.000000       2.200000  \n",
      "25%         2.000000       3.666667  \n",
      "50%         2.000000       6.666667  \n",
      "75%         3.000000      11.666667  \n",
      "max         5.000000      41.500000  \n",
      "           fre_peak    fre_nonpeak    traday_peak  traday_nonpeak  \\\n",
      "count  202701.00000  202701.000000  202701.000000   202701.000000   \n",
      "mean        7.38902       4.703079       4.284592        2.499075   \n",
      "std         5.15616       4.442507       0.861561        1.585767   \n",
      "min         1.00000       0.000000       1.000000        0.000000   \n",
      "25%         3.00000       1.333333       4.000000        1.000000   \n",
      "50%         6.20000       3.250000       5.000000        3.000000   \n",
      "75%        10.40000       7.000000       5.000000        4.000000   \n",
      "max        34.00000      41.000000       5.000000        5.000000   \n",
      "\n",
      "              traday        fre_avg  \n",
      "count  202701.000000  202701.000000  \n",
      "mean        4.680194       9.914220  \n",
      "std         0.466402       6.151218  \n",
      "min         4.000000       2.200000  \n",
      "25%         4.000000       4.400000  \n",
      "50%         5.000000       8.750000  \n",
      "75%         5.000000      14.400000  \n",
      "max         5.000000      30.200000  \n",
      "           fre_peak   fre_nonpeak   traday_peak  traday_nonpeak        traday  \\\n",
      "count  44661.000000  44661.000000  44661.000000    44661.000000  44661.000000   \n",
      "mean      24.920412     19.049270      4.298919        3.519178      4.547144   \n",
      "std       14.474131     14.320007      1.077073        1.383114      0.822380   \n",
      "min        0.000000      0.000000      0.000000        0.000000      2.000000   \n",
      "25%       16.400000     11.400000      4.000000        2.000000      4.000000   \n",
      "50%       22.000000     16.333333      5.000000        4.000000      5.000000   \n",
      "75%       29.500000     23.000000      5.000000        5.000000      5.000000   \n",
      "max      396.666667    333.000000      5.000000        5.000000      5.000000   \n",
      "\n",
      "            fre_avg  \n",
      "count  44661.000000  \n",
      "mean      38.842414  \n",
      "std       23.759899  \n",
      "min       10.200000  \n",
      "25%       27.200000  \n",
      "50%       32.333333  \n",
      "75%       41.200000  \n",
      "max      411.666667  \n"
     ]
    }
   ],
   "source": [
    "for i in range(3):\n",
    "    print(df_ana[df_ana['labels'] == i][['fre_peak','fre_nonpeak','traday_peak','traday_nonpeak','traday','fre_avg']].describe())"
   ]
  }
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